Computational Cognitive Science Documents

Showing 1 to 15 of 15

Summary
Structured representations are important
Abstract
Recursive
Generative
New primitive concepts can be learned
Learning the most parsimonious theory
How to combine structured representations and
statistical inference?
Statistical parsing in l

Outline for today
Learning a theory and new concepts in firstorder logic.
The debate about structure in peoples
mental representations of concepts
Hierarchies or hidden units?
Logical relations or hidden units?
Definitions or prototypes?
First-order

Where weve been, where were going
Two classes ago: classic model of learning
concepts based on combinations of features.
[Is it really learning? Fodors challenge.]
Theories of when learning is possible:
Identifiability in the limit: Subset principle

Goodmans problem
Why do some hypotheses receive
confirmation from examples but not others?
All piece of copper conduct electricity: yes
All men in this room are third sons: no
Distinguishing lawlike hypotheses from
accidental hypotheses is not easy:

9.66 / 9.914 Computational
Cognitive Science
Josh Tenenbaum
What is this class?
An attempt to see how recent work in
computation (AI, machine learning,
statistics) can inform some of the core
questions of cognitive science.
and vice versa.
The questions

Compression in Bayes nets
A Bayes net compresses the joint
probability distribution over a set of
variables in two ways:
Dependency structure
Parameterization
Both kinds of compression derive from
causal structure:
Causal locality
Independent causal

So.
why do we keep having this debate:
rules/symbols vs. prototypes/connections?
So.
The real problem: a spurious contest between
logic and probability.
Neither logic nor probability on its own is
sufficient to account for human cognition:
Generativity